Is 2018 the Year of Machine Learning for AdWords?

21 Nov, 2017

5 min. read

byTom Sadler

Working in marketing for the past 10 years has made me sceptical of the latest marketing fad – machine learning. I’ve been around long enough to remember everybody touting grandiose claims of “the year of mobile” in 2008 when in fact it took until 2014 for the mobile dream to be realised.

The last 2 years have given us bullshit bingo words such as “big data” and “programmatic”, the former being almost meaningless, and the latter being marred by ad fraud controversy. In the last 12 months, the buzzwords on everyone’s lips have been artificial intelligence and machine learning. So in this article, I’m going to look at artificial intelligence/machine learning and attempt to predict the impact that it will have on AdWords campaigns.So what level of impact will AI have? A huge one according to Google CEO, Sundar Pichai. He describes Google’s policy shift as, “An important shift from a mobile-first world to an AI-first world,” and it’s all about a transition, “searching and organising the world’s information to AI and machine learning”.

What is Machine Learning and AI?

Firstly, let’s look at some definitions so that we understand the difference between artificial intelligence and machine learning:

Artificial Intelligence – The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Machine Learning – Machine learning is an application of artificial intelligence (AI) which provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

How is Google Using it?

As we have seen, Google is taking machine learning very seriously. In 2014 they bought a UK company called DeepMind for US$525 million. In 2016 DeepMind lost US$162 million. Part of that loss was down to the development of Google’s machine learning computer program called AlphaGo. AlphaGo was designed to beat a professional human Go player (Go is a strategic board game in which one player must use moves to populate more space on the board than the other). In 2017 AlphaGo beat Ke Jie 3 nil – Ke Jie was the number 1 ranked player in the world!

Why is this significant?

I can remember a computer program called Deep Blue beating Gary Kasparov at chess in 1997. The significance comes from the complexity of Go. After the first 2 moves in chess there are 400 possible moves, but in Go, there are close to 130,000!

In a more directly relatable example, Google uses machine learning to aid speech recognition. Four years ago, Google had a 20% error rate when translating voice to text. Two years ago, this was an 8% error rate, and it’s now a 4.9 % error rate. The more data it feeds on, the better it gets. This is of particular relevance to AdWords as now 20% of all searches in the Google app are vocal.

How can you use machine learning and automation to improve your Google AdWords campaigns?

CPA Bidding – Whether you’re using the AdWords interface or an optimisation program such as DoubleClick or Marin, you can set up CPA bidding algorithms. These algorithms can save you time and effort in managing your CPC bids and modifiers for things such as device, location and interest.

Ad Customisers – If you’re not using Ad Customisers then you should be. These can be used to build out large amounts of keyword specific creative. Think dynamic search ads on steroids. Using this will enable you to automate the creation of a large number of adverts, giving Google machine learning algorithms more data in order to make better choices on which creative to search and when.

Similar Audiences – Google uses machine learning to build and continuously refine their similar audiences. Once again, the more data Google gets, the better these will become. You should be trialling similar audiences for your campaigns.

In-market Audiences – Google uses machine learning to predict when its users are in-market and ready to buy specific products. This is worth trialling on your clients’ campaigns, especially those running on the Google Display Network.

Attribution – Early next year Google will launch its free attribution tool. This will use machine learning to help suggest cross-channel budget adjustments. Make sure that you sign up as soon as this is available.

I expect that machine learning will have an ever-increasing impact on agency life. Low-level work such as pacing an account or managing CPC bids will become redundant. Agencies will be paid to focus less on execution and more on strategy. Those agencies that fail to adapt and use machine learning to its maximum capability will find themselves struggling to compete. If you are currently working in a digital agency, please take the time to learn about how machine learning can help your campaigns so that you can maximise this exciting technology change.

2018 might not be the year of machine learning but it’s definitely coming and you need to act now in order to not be left behind.